Abstract
At present, tourism is considered to be one of the key factors shaping the development of a country’s economy. Most of the tourists tend to explore places that they find fascinating after watching pictures of that places over Internet. Anyone can know about a famous place by simply typing the name of that place in an internet browser. But problem arises when he/she comes across the image of a beautiful landmark which is anonymous as most of the time web images do not convey any text caption. Most of models provided for image identification so far exhibit much complex structure and increased time complexity. In this paper, we have proposed a CNN model based on MobileNet and TensorFlow for detecting some historical landmarks of Bangladesh from their image. We have examined 750 images from five different places and comparing other state-of-art models, our model holds relatively simpler structure and has achieved a significantly higher average accuracy of 99.2%. This model can be further enhanced to facilitate image classification in other related areas.
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References
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions, arXiv:1409.4842v1 [cs.CV], 17 September 2014
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, arXiv:1602.07261 [cs.CV]
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J.: Rethinking the Inception Architecture for Computer Vision,arXiv:1512.00567v1 [cs.CV], 2 December 2015
Amin, K., Hussain, M., Ujang, N.: Visitors’ Identification of Landmarks in the Historic District of Banda Hilir, Melaka, Malaysia. In: AMER International Conference on Quality of Life, AicQoL2014KotaKinabalu
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Deep Learning with Tensorflow. http://cvml.ist.ac.at/courses/DLWT_W17/
Beynon, M.J., Jones, C., Munday, M., Roche, N.: Investigating value added from heritage assets: an analysis of landmark historical sites in Wales. Int. J. Tour. Res. 20(6), 756–767 (2018)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25 (NIPS 2012)
Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv:1409.1556v3 [cs.CV] 18 November 2014
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla,A., Bernstein, M., Berg, A.C., Li, F.-F.: ImageNet Large Scale Visual Recognition Challenge. arXiv:1409.0575v3 [cs.CV], 30 January 2015
Howar, A.G., Zhu,M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861v1 [cs.CV], 17 April 2017
Abadi, M., Agarwal, A., Barham, P., Brevdo, E.: Large-Scale Machine Learning on Heterogeneous Distributed Systems. (Preliminary White Paper, November 9, 2015), arXiv:1603.04467v2 [cs.DC], 16 March 2016
Kaiser, L., Gomez, A.N., Chollet, F.: Depthwise Separable Convolutions for Neural Machine Translation. arXiv:1706.03059v2 [cs.CL], 16 Jun 2017
https://www.docker.com/ Docker Simplifies the Developer Experience
The details of Confusion matrix. https://en.wikipedia.org/wiki/Confusion_matrix
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv:1512.03385v1 [cs.CV], 10 December 2015
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition arXiv:1409.1556v6 [cs.CV], 10 April 2015
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely Connected Convolutional Networks, arXiv:1608.06993v5 [cs.CV], 28 January 2018
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Jeny, A.A., Junayed, M.S., Atik, S.T., Mahamd, S. (2020). A Model for Identifying Historical Landmarks of Bangladesh from Image Content Using a Depth-Wise Convolutional Neural Network. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_41
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DOI: https://doi.org/10.1007/978-3-030-16657-1_41
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